1. bookVolumen 45 (2020): Edición 4 (December 2020)
Detalles de la revista
Primera edición
24 Oct 2012
Calendario de la edición
4 veces al año
access type Acceso abierto

Chessboard and Chess Piece Recognition With the Support of Neural Networks

Publicado en línea: 16 Dec 2020
Volumen & Edición: Volumen 45 (2020) - Edición 4 (December 2020)
Páginas: 257 - 280
Recibido: 30 May 2020
Aceptado: 12 Nov 2020
Detalles de la revista
Primera edición
24 Oct 2012
Calendario de la edición
4 veces al año

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. Digitization of a chess game state from a picture of a chessboard is a task typically performed by humans or with the aid of specialized chessboards and pieces. However, those solutions are neither easy nor convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations.

We designed a method of chessboard recognition and pieces detection that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. Detecting the board and recognizing chess pieces are crucial steps of board state digitization.

The algorithm achieves 95% accuracy (compared to 60% for the best alternative) for positioning the chessboard in an image, and almost 95% for chess pieces recognition. Furthermore, the sub-process of detecting straight lines and finding lattice points performs extraordinarily well, achieving over 99.5% accuracy (compared to the 74% for the best alternative).


[1] Acher M. and Esnault F. Large-scale analysis of chess games with chess engines: A preliminary report, 2016.Search in Google Scholar

[2] Arca S., Casiraghi E., and Lombardi G. Corner localization in chessboards for camera calibration. In Proceedings of International Conference on Multimedia, Image Processing and Computer Vision (IADAT-micv2005), 2005.Search in Google Scholar

[3] Bency A. J., Kwon H., Lee H., Karthikeyan S., and Manjunath B. Weakly supervised localization using deep feature maps. In European Conference on Computer Vision, pages 714–731. Springer, 2016.10.1007/978-3-319-46448-0_43Search in Google Scholar

[4] Bennett S. and Lasenby J. Chess–quick and robust detection of chess-board features. Computer Vision and Image Understanding, 118:197–210, 2014.10.1016/j.cviu.2013.10.008Search in Google Scholar

[5] Braje W. L., Kersten D., Tarr M. J., and Troje N. F. Illumination effects in face recognition. Psychobiology, 26(4):371–380, 1998.10.3758/BF03330623Search in Google Scholar

[6] Choudhury Z. H. Biometrics security based on face recognition. Master’s thesis, India, 2013.Search in Google Scholar

[7] CoolThings. Square off is a robot chess board that can move pieces on its own, November 2016.Search in Google Scholar

[8] Cour T., Lauranson R., and Vachette M. Autonomous chess-playing robot. Ecole Poly-technique, July, 2002.Search in Google Scholar

[9] Czyzewski M. A., Laskowski A., and Wasik S. Latchess21: dataset of damaged chessboard lattice points (chessboard features) used to train laps detector (grayscale/21x21px), 2018.Search in Google Scholar

[10] Danner C. and Kafafy M. Visual chess recognition, 2015.Search in Google Scholar

[11] De la Escalera A. and Armingol J. M. Automatic chessboard detection for intrinsic and extrinsic camera parameter calibration. Sensors, 10(3):2027–2044, 2010.10.3390/s100302027326446522294912Search in Google Scholar

[12] Ding J. Chessvision: Chess board and piece recognition. Technical report, Stanford University, 2016.Search in Google Scholar

[13] Duda R. O. and Hart P. E. Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15, Jan. 1972.10.1145/361237.361242Search in Google Scholar

[14] Edwards S. J. Portable game notation specification and implementation guide. Retrieved April, 4:2011, 1994.Search in Google Scholar

[15] Fernandes L. A. and Oliveira M. M. Real-time line detection through an improved hough transform voting scheme. Pattern recognition, 41(1):299–314, 2008.10.1016/j.patcog.2007.04.003Search in Google Scholar

[16] Galler B. A. and Fisher M. J. An improved equivalence algorithm. Commun. ACM, 7(5):301–303, May 1964.10.1145/364099.364331Search in Google Scholar

[17] Gao F., Huang T., Wang J., Sun J., Hussain A., and Yang E. Dual-branch deep convolution neural network for polarimetric SAR image classification. Applied Sciences, 7(5):447, 2017.10.3390/app7050447Search in Google Scholar

[18] Hamid N. and Khan N. Lsm: perceptually accurate line segment merging. Journal of Electronic Imaging, 25(6):061620, 2016.Search in Google Scholar

[19] Harris C. and Stephens M. A combined corner and edge detector. In Alvey vision conference, volume 15, pages 10–5244. Citeseer, 1988.10.5244/C.2.23Search in Google Scholar

[20] Jassim F. A. and Altaani F. H. Hybridization of otsu method and median filter for color image segmentation, 2013.Search in Google Scholar

[21] Kanchibail R., Suryaprakash S., and Jagadish S. Chess board recognition. Not published in journal, 2016.Search in Google Scholar

[22] Koray C. and Sumer E. A computer vision system for chess game tracking. In 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia, 2016.Search in Google Scholar

[23] Larson C. China’s massive investment in artificial intelligence has an insidious downside. Science, feb 2018.Search in Google Scholar

[24] Leonard J., Durrant-Whyte H., and Cox I. Dynamic map building for autonomous mobile robot. In IEEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications. IEEE, jul 1990.Search in Google Scholar

[25] Li Q., Zheng N., and Cheng H. Springrobot: A prototype autonomous vehicle and its algorithms for lane detection. IEEE Transactions on Intelligent Transportation Systems, 5(4):300–308, dec 2004.10.1109/TITS.2004.838220Search in Google Scholar

[26] Lu X., Yao J., Li K., and Li L. Cannylines: A parameter-free line segment detector. In Image Processing (ICIP), 2015 IEEE International Conference on, pages 507–511. IEEE, 2015.10.1109/ICIP.2015.7350850Search in Google Scholar

[27] Marciniak T., Chmielewska A., Weychan R., Parzych M., and Dabrowski A. Influence of low resolution of images on reliability of face detection and recognition. Multimedia Tools and Applications, 74(12):4329–4349, jul 2013.10.1007/s11042-013-1568-8Search in Google Scholar

[28] Matuszek C., Mayton B., Aimi R., Deisenroth M. P., Bo L., Chu R., Kung M., LeGrand L., Smith J. R., and Fox D. Gambit: An autonomous chess-playing robotic system. 2011 IEEE International Conference on Robotics and Automation, pages 4291–4297, 2011.Search in Google Scholar

[29] Mietchen D., Wodak S., Wasik S., Szostak N., and Dessimoz C. Submit a topic page to plos computational biology and wikipedia. PLOS Computational Biology, 14(5):1–4, 05 2018.10.1371/journal.pcbi.1006137597887729851950Search in Google Scholar

[30] Pomerleau D. and Jochem T. Rapidly adapting machine vision for automated vehicle steering. IEEE Expert, 11(2):19–27, apr 1996.10.1109/64.491277Search in Google Scholar

[31] Prejzendanc T., Wasik S., and Blazewicz J. Computer representations of bioinformatics models. Current Bioinformatics, 11(5):551–560, 2016.10.2174/1574893610666150928193510Search in Google Scholar

[32] Reza A. M. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. Journal of VLSI signal processing systems for signal, image and video technology, 38(1):35–44, 2004.10.1023/B:VLSI.0000028532.53893.82Search in Google Scholar

[33] Sen D. and Pal S. K. Gradient histogram: Thresholding in a region of interest for edge detection. Image and Vision Computing, 28(4):677–695, 2010.Search in Google Scholar

[34] Shortis M. Calibration techniques for accurate measurements by underwater camera systems. Sensors, 15(12):30810–30826, 2015.10.3390/s151229831472175126690172Search in Google Scholar

[35] Soh L. Robust recognition of calibration charts. In 6th International Conference on Image Processing and its Applications. IEE, 1997.10.1049/cp:19970941Search in Google Scholar

[36] Stark J. A. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing, 9(5):889–896, May 2000.10.1109/83.84153418255459Search in Google Scholar

[37] Szostak N., Wasik S., and Blazewicz J. Hypercycle. PLOS Computational Biology, 12(4):e1004853, apr 2016.10.1371/journal.pcbi.1004853482441827054759Search in Google Scholar

[38] Tam K. Y., Lay J. A., and Levy D. Automatic grid segmentation of populated chess-board taken at a lower angle view. In Computing: Techniques and Applications, 2008. DICTA’08. Digital Image, pages 294–299. IEEE, 2008.10.1109/DICTA.2008.40Search in Google Scholar

[39] Tarjan R. E. Efficiency of a good but not linear set union algorithm. J. ACM, 22(2):215–225, Apr. 1975.10.1145/321879.321884Search in Google Scholar

[40] Tavares J. M. R. S. and Padilha A. J. M. N. A new approach for merging edge line segments. Proceedings RecPad’95, Aveiro, 1995.Search in Google Scholar

[41] Urting D. and Berbers Y. Marineblue: A low-cost chess robot. In Robotics and Applications, 2003.Search in Google Scholar

[42] Wasik S., Fratczak F., Krzyskow J., and Wulnikowski J. Inferring Mathematical Equations Using Crowdsourcing. PLOS ONE, 10(12):e0145557, dec 2015.10.1371/journal.pone.0145557Search in Google Scholar

[43] Wasik S., Prejzendanc T., and Blazewicz J. ModeLang - a new approach for experts-friendly viral infections modeling. Computational and Mathematical Methods in Medicine, 2013:8, 2013.10.1155/2013/320715Search in Google Scholar

[44] Wiens D. P. Asymptotics of generalized m-estimation of regression and scale with fixed carriers, in an approximately linear model. Statistics & probability letters, 30(3):271–285, 1996.10.1016/0167-7152(95)00230-8Search in Google Scholar

[45] Wu Q., Zhang J., Lai Y.-K., Zheng J., and Cai J. Alive caricature from 2d to 3d, 2018.10.1109/CVPR.2018.00766Search in Google Scholar

[46] Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11):1330–1334, 2000.10.1109/34.888718Search in Google Scholar

[47] Zhao F., Wei C., Wang J., and Tang J. An automated x-corner detection algorithm (axda). JSW, 6(5):791–797, 2011.10.4304/jsw.6.5.791-797Search in Google Scholar

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